Columns and types
VARCHAR
- ticker
DATE
- date
BIGINT
- minute_ns
DOUBLE
- bid_price
- ask_price
INTEGER
- bid_size
- ask_size
Current logical schema for lake.intraday_options_quotes
Inspect the complete lake.intraday_options_quotes schema
| Column | Type |
|---|---|
| ticker | VARCHAR |
| date | DATE |
| minute_ns | BIGINT |
| bid_price | DOUBLE |
| ask_price | DOUBLE |
| bid_size | INTEGER |
| ask_size | INTEGER |
No matching rows. Clear the filter to see all records.
Current logical schema for lake.intraday_options_quotes
Query lake.intraday_options_quotes using the column names and types below. The query validator and engine use this logical catalog; a field absent from an older shard returns typed NULL.
What this table contains
Raw OPRA NBBO at (ticker, minute) grain, whether or not a trade printed — use for spread studies, where trade-grain sources under-sample wide quotes. Requires a literal `date` range and a `ticker` filter. ~1 GiB and ~289M rows per day, one object per day, so the range decides what is read: e.g. `date BETWEEN DATE '2026-09-15' AND DATE '2026-09-19'`; unbounded queries are rejected. Coverage starts 2022-03-07. For coverage questions, bound a window or use lake.options_daily (completed session-boundary NBBO).
Inspect one actual option contract NBBO
Query example, not executed here. The dates and filters define the proposed research scope; returned rows and current coverage must be checked in your workspace.
First retrieve a contract ticker from a bounded options_daily query for the desired underlying and dates. Copy that full ticker into the literal before submitting this template. ticker here identifies the contract, not AAPL as an underlying. Bid and ask describe the quoted book; minutes without trades can still appear.
-- Replace the literal below with a contract ticker returned by options_daily.
SELECT ticker, date, minute_ns, bid_price, ask_price, bid_size, ask_size
FROM lake.intraday_options_quotes
WHERE ticker = 'REPLACE_WITH_COVERED_OCC_TICKER'
AND date >= DATE '2026-09-15'
AND date < DATE '2026-09-19'
ORDER BY date, minute_ns
LIMIT 20;Submit the intraday_options_quotes example and read back its parts
Run the exact SQL through the Python SDK
Save this page's SQL as query.sql and the runner below as query.py. Use a registered key with lake scope from Developers. The runner submits the displayed intraday_options_quotes query once, saves its operation, waits on that same ID, saves the returned manifest and downloads its Parquet parts.
This submission has not been executed for the reference page. Inspect the SQL, dates and resource limits before running it. Reuse the request key only for this identical SQL and limits; use a new key when changing them.
python3 -m pip install nexustrade==1.42.0
export NEXUSTRADE_API_KEY="YOUR_LAKE_SCOPED_KEY"
python3 query.py
import json
from pathlib import Path
from nexustrade import NexusTradeClient
# Save the SQL displayed on this page as query.sql.
sql = Path("query.sql").read_text()
client = NexusTradeClient.from_environment()
operation = client.create_lake_query(
{"query": sql, "limits": {"timeoutSeconds": 30, "maxRows": 50,
"maxResultBytes": 1048576}},
idempotency_key="reference-intraday_options_quotes-20261012-v1",
)
Path("query-operation.json").write_text(json.dumps(operation, indent=2))
finished = client.wait_for_lake_query(operation["id"])
if finished["status"] != "completed":
raise RuntimeError(finished)
manifest = client.get_lake_query_manifest(operation["id"])["result"]
Path("query-manifest.json").write_text(json.dumps(manifest, indent=2))
print(json.dumps(manifest, indent=2))
for part in manifest["parts"]:
Path(f"query-{part['part']}.parquet").write_bytes(
client.download_lake_query_part(operation["id"], part["part"], max_bytes=1048576)
)
Coverage and timing
Inspect source coverage and timing before using the rows
Raw OPRA NBBO at (ticker, minute) grain, whether or not a trade printed — use for spread studies, where trade-grain sources under-sample wide quotes. Requires a literal `date` range and a `ticker` filter. ~1 GiB and ~289M rows per day, one object per day, so the range decides what is read: e.g. `date BETWEEN DATE '2026-09-15' AND DATE '2026-09-19'`; unbounded queries are rejected. Coverage starts 2022-03-07. For coverage questions, bound a window or use lake.options_daily (completed session-boundary NBBO).
Inspect the returned source dates and nullable fields. For an event-based strategy, use its public availability date to place the observation on the research timeline.
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